# Distributed Training with Amazon SageMaker RL<a name="sagemaker-rl-distributed"></a>

Amazon SageMaker RL supports multi\-core and multi\-instance distributed training\. Depending on your use case, training and/or environment rollout can be distributed\. For example, SageMaker RL works for the following distributed scenarios:
+ Single training instance and multiple rollout instances of the same instance type\. For an example, see the Neural Network Compression example in the [SageMaker examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning)\.
+ Single trainer instance and multiple rollout instances, where different instance types for training and rollouts\. For an example, see the AWS DeepRacer / AWS RoboMaker example in the [SageMaker examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning)\.
+ Single trainer instance that uses multiple cores for rollout\. For an example, see the Roboschool example in the [SageMaker examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning)\. This is useful if the simulation environment is light\-weight and can run on a single thread\. 
+ Multiple instances for training and rollouts\. For an example, see the Roboschool example in the [SageMaker examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning)\.